Unsupervised Bayesian segmentation using hidden Markovian fields
F. Salzenstein, Wojciech Pieczynski · 2002
The aim of our paper is to present a new unsupervised Bayesian image segmentation method using a recent model by hidden fuzzy Markov fields. The main problem of parameter estimation is solved using a recent general method of estimation regarding hidden data, called iterative conditional estimation (ICE). This has been successfully applied in classical hidden Markov fields based segmentations. The first part of our work involves estimating the parameters defining the Markovian distribution of the fuzzy picture without noise. We then combine this algorithm with the ICE method in order to estimate all the parameters of the noisy picture.